Identifying At-Risk Snow Water Resources In The Great Basin

Snowmelt provides water for over a billion people globally and greater than 80% of the water resources for the State of Nevada. These critical resources face an uncertain future as regional warming has the potential shift snow to rain, increase the energy available to melt snowpacks and intensify water demand from vegetation and the atmosphere. The large hydro-climatic gradient across the Great Basin is likely to result in uneven snowpack response to warming temperatures with unknown consequences for ecosystem services and downstream water resources.

Determining future snow water availability requires quantifying the counteracting snowpack processes that promote greater water vapor loss (e.g. snowpack storage and release during high water demand) with those processes that promote greater water subsidy (e.g. more prolonged infiltration events and greater subsurface pressure heads). Predicting the effects of climate change on snowmelt-mediated hydrological processes is a critical scientific challenge for water security in the Western U.S.

This project investigates how changing snowpacks are modulated by landscape hydrological processes through a multi-scale approach that integrates field observations, remote sensing of snow and vegetation, and physically-based modeling.

Completion of the project offers numerous economic and social benefits for the residents of Nevada. For example, the development of a new drought index that includes snowpack storage could provide early warning of water shortages in areas with limited observations. Satellite estimates of vegetation health will be used to identify Great Basin ecosystems and vegetation types vulnerable to altered snow water availability. The project will also link changing streamflow to downstream water rights and economic productivity to increase information available to policy makers.

Predicting the effects of changing snowpack dynamics on water availability for vegetation and streamflow is a grand challenge for the hydrological sciences. This challenge partially stems from locally variable response of snowpacks to warming that is difficult to model and predict. Quantifying the processes that govern the pedon to watershed-scale hydrological effects from changing snowpack dynamics poses additional challenges. There is ongoing research in both the snow and watershed hydrology communities investigating these processes, yet comprehensive understanding of future water availability in snow dominated landscapes will require coupling variability in snowpack and physical hydrology across spatial scales in ways that remain largely unexplored.

Observations of local variability in snowpack dynamics portend uneven impacts to climate change [Mankin and Diffenbaugh, 2014]. For example, change in snowmelt timing has been considerable in the warmer, maritime regions [Mote et al., 2005, Regonda et al., 2005], but much more locally variable in the colder, continental snowpacks [Knowles et al., 2006; Regonda et al., 2005; Harpold et al., 2012]. Whether these local changes in snowmelt timing will be accompanied by decreases in snowmelt rate [Trujillo and Molotch, 2014] or increases in snowmelt rate [Harpold et al., 2012] are likely to have substantial impacts on snowmelt infiltration. Winter season ablation (e.g. sublimation and melt before peak snow accumulation) also varies across Western U.S regions, with observations of high-elevation ablation in continental snowpacks [Harpold et al., 2012]. Changes from winter snow to rain have also been locally variable over the last 50 years [Knowles et al., 2005]. While much of this variability in precipitation phase change is attributable to air temperatures [Hamlet et al., 2005, Knowles et al., 2005, Klos et al., 2014], the importance of other atmospheric conditions [Harder and Pomeroy, 2013] on local differences is relatively unknown. Improved understanding of interactions between of hydroclimate (e.g. temperature, vapor pressure deficit, precipitation amount and timing, etc.) and physiographic properties (e.g. topography, vegetation cover, etc.) remains critical to deciphering the inter-regional and local variability in snowpack processes and their corresponding vulnerability to change [Mankin and Diffenbaugh, 2014].

In conjunction with observations, empirical and physically-based models offer another tool to estimate local drought and snowmelt-streamflow connections. Drought indices are empirical models that account for precipitation inputs relative to water demand to assess water availability. Most drought indices currently in widespread use do not consider snow storage and melt, which is problematic for predicting the effects of changing snowpack dynamics on drought [McEvoy et al., 2012; Haslinger et al., 2014]. Efforts to integrate snowpack processes into drought models have been data intensive [McKee et al., 1993] or focused on warmer, humid snowpacks [Staudinger et al., 2014], which has limited their application to semi-arid mountain areas. Many drought models have also applied temperature-based estimates of PET [e.g. McEvoy et al., 2012] that may be inappropriate for snow-covered areas experiencing climate change [Sheffield et al., 2014]. Physically-based models rely on observations to calibrate processes and estimate atmospheric forcings [Elsner et al., 2014], which has hampered their acceptance over simpler, degree-day models used extensively for water resource prediction [Tobin et al., 2013]. A new generation of data assimilation based snowpack models show promise for integrating field and remote sensing snowpack observations into energy-based models. One such model, the SNOw Data Assimilation System (SNODAS), has been verified in diverse hydroclimates [Clow et al., 2012; Hedrick et al., 2014; Boniface et al., 2014] and is beginning to be applied to estimate watershed-scale snowpack variability. Models provide an important tool to estimate vulnerability to drought and altered snowpacks, but require verification across diverse environmental conditions.

A lack of characterization of the hydrological processes that store and transport snowmelt limits estimating the impacts of snowpack variation and change in topographically complex landscapes [NRC, 2012]. Strong observational and modeling evidence suggests that pedon-scale snow water partitioning influences vegetation productivity and water stress [Hu et al., 2010; Tague and Peng, 2013]. Few studies, however, have detailed how soil properties and snowmelt dynamics interact to control the partitioning of snowmelt to runoff versus ET at the pedon-scale [Kormos et al., 2014] across sites. Instead, research efforts have applied observations and models to link watershed-scale topography and geologic features to streamflow, which aggregates or neglects smaller-scale processes. This previous watershed-scale work has shown that snowpack-streamflow connections are strongly mediated by geological variations controlling the fraction of deep and shallow hydrological flowpaths [Tague and Grant, 2009; Huntington and Niswonger, 2014; Safeeq et al., 2013]. Similarly, the underlying geology and groundwater inputs influence the effects of changes in snow to rain on groundwater storage [Huntington and Niswonger, 2012; Godsey et al., 2013] and streamflow [Safeeq et al., 2014]. Unfortunately, many of these watershed-scale inferences lack corroborating evidence from more detailed observations. Without a more robust physical framework, observations and modeling of snowpack-streamflow connections remain difficult to explain physically [Berghuijs et al., 2014, Godsey et al., 2013] and thus, provide marginal utility for improving local-scale water and forest management [Grant et al., 2013].

Our current scientific paradigm lacks a process-based framework to connect the interactions between snowpack dynamics and water partitioning at the pedon-scale with more common observations of streamflow and vegetation response at larger scales [Garcia and Tague, 2014]. The diverse hydroclimates of the Great Basin provide an ideal setting to investigate regional snowpack variations and the consequences for water availability. The Great Basin is dominated by winter precipitation (Figure 1b) but hydroclimates vary strongly in terms of temperature, humidity, and precipitation from the wet, warm Sierra Nevada to the cold, dry Great Basin ranges (Figure 1a and 1c). This has led to unpredictable local variation in snowpack response to warming temperatures over the last 30 years [Harpold et al., 2012; Mankin and Diffenbaugh, 2014]. A unique dataset of co-located soil and snowpack measurements across the Great Basin offers the potential to understand how snowmelt and soil-mediated processes control hydrologic partitioning to vegetation and runoff at the pedon-scale. These process-based findings can be contrasted with streamflow partitioning from a corresponding network of Great Basin watersheds. The datasets used in the hydrological partitioning efforts will form the basis of a new drought index for snow-dominated watersheds better suited to predicting the role of changing snowpacks. Ultimately, the value of this information for ecological and water resource management needs to be quantified relative to historical water rights and ecological services in water-limited areas.

The long-term goal of this project is to improve water management decision-making by identifying Great Basin watersheds that are most at-risk for altered ecological functioning and downstream water availability as a consequence of changing snowpack dynamics. The project will meet this goal with four supporting objectives:

Quantify the response of snowpack dynamics and streamflow partitioning to short and long-term climate variations. The response of snow and streamflow to inter-annual and decadal variations in hydroclimate is an indicator of future vulnerability. Simple hydrologic partitioning frameworks will provide insight into snow water availability under different hydroclimatic and snowpack regimes. We will quantify trends in snowpack and streamflow over a variety of time scales and identify the physiographic characteristics that mediate vulnerability to change.

Perform detailed soil moisture modeling to determine snowpack dynamics and soil properties that increase vulnerability to changes in hydrologic partitioning. We still lack a process-based understanding of how earlier and/or faster snowmelts and shifts in snow to rain will impact streamflow. The proposed objective will use a soil moisture model parameterized with measured soil hydraulic properties and snowmelt to investigate the sensitivity of deep drainage to changing snowpack regimes. These detailed results will build a framework to inform differences in watershed-scale hydrologic partitioning.

Develop a drought index that accounts for snow water storage and variable energy inputs. Current drought indices do not effectively account for snow water storage and apply potential evapotranspiration (PET) estimates that do not capture the range of hydroclimates found in the Great Basin. This objective will identify the value of including snowpacks in drought indices across diverse Great Basin watersheds. A newly developed drought index will be verified using observed streamflow and transferrable to areas with limited observations.

Connect sensitivity of changing snow water availability with vegetation response and downstream water demand. Identifying water resources vulnerable to changing snowpacks is fundamental to developing effective management strategies. The consequences for downstream water rights and populations will be quantified using previously developed hydrologic partitioning datasets and the new drought index. Hydrologic partitioning results at the pedon- and watershed-scales will be related to remote sensing observations of vegetation productivity.

Grant, G. E., Tague, C. L., and Allen, C. D. (2013). Watering the forest for the trees: an emerging priority for managing water in forest landscapes. Frontiers in Ecology and the Environment, 11(6): 314-321.

Tague, C., and Peng, H. (2013). The sensitivity of forest water use to the timing of precipitation and snowmelt recharge in the California Sierra: Implications for a warming climate. Journal of Geophysical Research: Biogeosciences, 118(2): 875-887.